README.md

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title: EasyDeL
emoji: 🔮
colorFrom: purple
colorTo: blue
sdk: static
pinned: false
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# EasyDeL

EasyDeL is an open-source framework for building, training, fine-tuning, converting, and serving modern ML models in **JAX** at scale. It is designed for people who want **the performance benefits of JAX** without giving up the **practical ergonomics** of the Hugging Face ecosystem.

## Purpose

JAX is extremely powerful, but scaling real training/inference workloads can still feel fragmented: model code, sharding, kernels, training loops, serving, and conversions often live in separate places. EasyDeL’s goal is to provide a cohesive toolkit where these pieces work together—while still staying readable and hackable.

## What EasyDeL focuses on

- **Scale-first**: multi-device training/inference across GPU/TPU with sharding-aware utilities.
- **Production inference**: a dedicated serving stack built for throughput and low latency.
- **Interoperability**: straightforward workflows with Hugging Face models and assets.
- **Hackability**: implementations you can actually read, debug, and modify.